library(tidyverse)
## ── Attaching packages ───────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ──────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(ggridges)
weather_df =
rnoaa::meteo_pull_monitors(
c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(
id,
USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())
weather_df
## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # … with 1,085 more rows
Create my first scatterplot ever.
ggplot(weather_df,aes(x = tmin, y = tmax)) +
geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).
New approach, same plot
weather_df %>%
ggplot(aes(tmin, tmax)) +
geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).
save and edit a plot object
plot_weather =
weather_df %>%
ggplot(aes(x = tmin, y = tmax))
plot_weather + geom_point(alpha = .5)
## Warning: Removed 15 rows containing missing values (geom_point).
Start with the same one and made it fancy
weather_df %>%
ggplot(aes(tmin, tmax,color = name)) +
geom_point(alpha = .5) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
what about the ‘aes’ placement
weather_df %>%
ggplot(aes(tmin, tmax)) +
geom_point(aes(color = name)) +
geom_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
facet same thing
weather_df %>%
ggplot(aes(tmin, tmax,color = name)) +
geom_point(alpha = .5) +
geom_smooth(se = FALSE) +
facet_grid(. ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
combine some elements and tey a new plot
ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_point(aes(size = prcp), alpha = .5) +
geom_smooth(se = FALSE) +
facet_grid(. ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
Write a code chain that starts with weather_df; focuses only on Central Park, converts temperatures to Fahrenheit, makes a scatterplot of min vs. max temperature, and overlays a linear regression line (using options in geom_smooth()).
weather_df %>%
filter(name == "CentralPark_NY") %>%
mutate(
tmax_fahr = tmax * (9 / 5) + 32,
tmin_fahr = tmin * (9 / 5) + 32) %>%
ggplot(aes(x = tmin_fahr, y = tmax_fahr)) +
geom_point(alpha = .5) +
geom_smooth(method = "lm", se = FALSE)
## `geom_smooth()` using formula 'y ~ x'
Looks like there’s a pretty linear relationship between min and max temperatures in Central Park.
how many geoms have to exist?
You can have whatever geoms you want
weather_df %>%
ggplot(aes(tmin,tmax,color = name)) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
You can use a neat geom!
weather_df %>%
ggplot(aes(tmin,tmax)) +
geom_hex()
## Warning: Removed 15 rows containing non-finite values (stat_binhex).
weather_df %>%
ggplot(aes(tmin,tmax)) +
geom_bin2d()
## Warning: Removed 15 rows containing non-finite values (stat_bin2d).
weather_df %>%
ggplot(aes(tmin,tmax)) +
geom_density_2d() +
geom_point(alpha = .3)
## Warning: Removed 15 rows containing non-finite values (stat_density2d).
## Warning: Removed 15 rows containing missing values (geom_point).
ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin), color = "blue")
## Warning: Removed 15 rows containing missing values (geom_point).
ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin, color = "blue"))
## Warning: Removed 15 rows containing missing values (geom_point).
histograms
weather_df %>%
ggplot(aes(tmin)) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 15 rows containing non-finite values (stat_bin).
add color
weather_df %>%
ggplot(aes(tmin, fill = name)) +
geom_histogram(position = 'dodge')
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 15 rows containing non-finite values (stat_bin).
weather_df %>%
ggplot(aes(tmin, fill = name)) +
geom_histogram(position = 'dodge') +
facet_grid(. ~name)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 15 rows containing non-finite values (stat_bin).
new geometry
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_density(alpha = .4, adjust = .5, color = "blue")
## Warning: Removed 3 rows containing non-finite values (stat_density).
box plot
weather_df %>%
ggplot(aes(x = name, y = tmax)) +
geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
trendy plots
weather_df %>%
ggplot(aes(x = name, y = tmax)) +
geom_violin(aes(fill = name), alpha = .5) +
stat_summary(fun = "median", color = "blue")
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
## Warning: Removed 3 rows containing non-finite values (stat_summary).
## Warning: Removed 3 rows containing missing values (geom_segment).
ridgeplots
weather_df %>%
ggplot(aes(x = tmax, y = name)) +
geom_density_ridges(scale = .85)
## Picking joint bandwidth of 1.84
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).
save
weather_plot =
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5)
ggsave("./result/weather_plot.pdf", weather_plot, width = 8, height = 5)
## Warning: Removed 15 rows containing missing values (geom_point).
embeding
weather_plot
## Warning: Removed 15 rows containing missing values (geom_point).
weather_plot
## Warning: Removed 15 rows containing missing values (geom_point).